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slug, title, type, description, shortDescription, publishedAt, readingTime, status, tags, icon
| slug | title | type | description | shortDescription | publishedAt | readingTime | status | tags | icon | |||||
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| hackathon-natixis | Natixis Hackathon: Generative SQL Analytics | Hackathon | An intensive 4-week challenge to build an AI-powered data assistant. Our team developed a GenAI agent that transforms natural language into executable SQL queries, interactive visualizations, and natural language insights. | A team-based project building an NL-to-SQL agent with Nuxt, Ollama, and Vercel AI SDK. | 2026-03-07 | 4 | Completed |
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i-ph-database-duotone |
::BackgroundTitle{title="The Challenge"} ::
Organized by Natixis, this hackathon followed a high-intensity format: three consecutive Saturdays of on-site development, bridged by two full weeks of remote collaboration.
Working in a team of four, our goal was to bridge the gap between non-technical stakeholders and complex financial databases by creating an autonomous "Data Talk" agent.
::BackgroundTitle{title="Core Features"} ::
1. Data Engineering & Schema Design
Before building the AI layer, we handled a significant data migration task. I led the effort to:
- ETL Pipeline: Convert fragmented datasets from .xlsx and .csv formats into a structured SQL database.
- Schema Optimization: Design robust SQL schemas that allow an LLM to understand relationships (foreign keys, indexing) for accurate query generation.
2. Natural Language to SQL (NL-to-SQL)
Using the Vercel AI SDK and Ollama, we implemented an agentic workflow:
- Prompt Engineering: Fine-tuning the agent to translate complex business questions (e.g., "What was our highest growth margin last quarter?") into valid, optimized SQL.
- Self-Correction: If a query fails, the agent analyzes the SQL error and self-corrects the syntax before returning a result.
3. Automated Insights & Visualization
Data is only useful if it’s readable. Our Nuxt application goes beyond raw tables:
- Dynamic Charts: The agent automatically determines the best visualization type (Bar, Line, Pie) based on the query result and renders it using interactive components.
- Narrative Explanations: A final LLM pass summarizes the data findings in plain English, highlighting anomalies or key trends.
::BackgroundTitle{title="Technical Stack"} ::
- Frontend/API: Nuxt 3 for a seamless, reactive user interface.
- Orchestration: Vercel AI SDK to manage streams and tool-calling logic.
- Inference: Ollama for running LLMs locally, ensuring data privacy during development.
- Storage: PostgreSQL for the converted data warehouse.
::BackgroundTitle{title="Impact & Results"} ::
This project demonstrated that a modern stack (Nuxt + local LLMs) can drastically reduce the time needed for data discovery. By the final Saturday, our team presented a working prototype capable of handling multi-table joins and generating real-time financial dashboards from simple chat prompts.
Curious about the ETL logic or the prompt structure we used? I can share how we optimized the LLM's SQL accuracy.